A Bayesian quantile regression framework for scaling precipitation with temperature and weather regimes in the Northeast US
Abstract
The intensification of precipitation with warming is a major impact of climate change and is driven by the increased moisture holding capacity of the air as temperatures rise. The rate of intensification (i.e., the percent increase in precipitation for every degree of warming) is an important scientific question with major implications for how communities should adapt to climate change. We present a novel hierarchical Bayesian quantile regression model to estimate how precipitation scales with temperature depending on season, weather regime (WR; i.e., large-scale patterns of atmospheric circulation), and precipitation percentile. The approach develops regional scaling estimates by partially pooling data across sites, accounting for uncertainty stemming from variable record lengths. Results using long-term observations of daily precipitation and both dry-bulb and dew point temperature across 93 weather stations in the Northeast US suggest that regional precipitation scaling rates vary from 3-10% per degree C. Scaling rate variability is driven most by season and the percentile of precipitation under consideration, with only moderate variations across WRs. Scaling rates are highest in the winter and summer, especially for lower percentiles (e.g., the median). Lower scaling rates for more extreme precipitation quantiles are driven by a handful of storms occurring at cooler temperatures but with strong vertical uplift and heavy precipitation.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.A25H1761N